Current domain adaptation methods for face anti-spoofing leverage labeled source domain data and unlabeled target domain data to obtain a promising generalizable decision boundary. However, it is usually difficult for these methods to achieve a perfect domain-invariant liveness feature disentanglement, which may degrade the final classification performance by domain differences in illumination, face category, spoof type, etc. In this work, we tackle cross-scenario face anti-spoofing by proposing a novel domain adaptation method called cyclically disentangled feature translation network (CDFTN). Specifically, CDFTN generates pseudo-labeled samples that possess: 1) source domain-invariant liveness features and 2) target domain-specific content features, which are disentangled through domain adversarial training. A robust classifier is trained based on the synthetic pseudo-labeled images under the supervision of source domain labels. We further extend CDFTN for multi-target domain adaptation by leveraging data from more unlabeled target domains. Extensive experiments on several public datasets demonstrate that our proposed approach significantly outperforms the state of the art.

In the current person Re-identification (ReID) methods, most domain generalization works focus on dealing with style differences between domains while largely ignoring unpredictable camera view change, which we identify as another major factor leading to a poor generalization of ReID methods. To tackle the viewpoint change, this work proposes to use a 3D dense pose estimation model and a texture mapping module to map the pedestrian images to canonical view images. Due to the imperfection of the texture mapping module, the canonical view images may lose the discriminative detail clues from the original images, and thus directly using them for ReID will inevitably result in poor performance. To handle this issue, we propose to fuse the original image and canonical view image via a transformer-based module. The key insight of this design is that the cross-attention mechanism in the transformer could be an ideal solution to align the discriminative texture clues from the original image with the canonical view image, which could compensate for the low-quality texture information of the canonical view image. Through extensive experiments, we show that our method can lead to superior performance over the existing approaches in various evaluation settings.

很少有分段旨在学习一个细分模型，该模型可以推广到只有几个培训图像的新课程。在本文中，我们提出了一个交叉引用和局部全球条件网络（CRCNET），以进行几次分割。与以前仅预测查询图像掩码的作品不同，我们提出的模型同时对支持图像和查询图像进行了预测。我们的网络可以更好地在两个图像中使用交叉引用机制找到同时出现的对象，从而有助于少量分割任务。为了进一步改善功能比较，我们开发了一个局部全球条件模块，以捕获全球和本地关系。我们还开发了一个掩模修补模块，以重新完善前景区域的预测。Pascal VOC 2012，MS Coco和FSS-1000数据集的实验表明，我们的网络实现了新的最新性能。

尽管参数有效调整（PET）方法在自然语言处理（NLP）任务上显示出巨大的潜力，但其有效性仍然对计算机视觉（CV）任务的大规模转向进行了研究。本文提出了Conv-Adapter，这是一种专为CONCNET设计的PET模块。 Conv-Adapter具有轻巧的，可转让的域和架构，不合时宜，并且在不同的任务上具有广义性能。当转移下游任务时，Conv-Adapter将特定于任务的特征调制到主链的中间表示，同时保持预先训练的参数冻结。通过仅引入少量可学习的参数，例如，仅3.5％的RESNET50的完整微调参数，Conv-Adapter优于先前的宠物基线方法，并实现可比性或超过23个分类任务的全面调查的性能。它还在几乎没有分类的情况下表现出卓越的性能，平均利润率为3.39％。除分类外，Conv-Adapter可以推广到检测和细分任务，其参数降低了50％以上，但性能与传统的完整微调相当。

很少有开放式识别旨在对可见类别的培训数据进行有限的培训数据进行分类和新颖的图像。这项任务的挑战是，该模型不仅需要学习判别性分类器，以用很少的培训数据对预定的类进行分类，而且还要拒绝从未见过的培训时间出现的未见类别的输入。在本文中，我们建议从两个新方面解决问题。首先，我们没有像在标准的封闭设置分类中那样学习看到类之间的决策边界，而是为看不见的类保留空间，因此位于这些区域中的图像被认为是看不见的类。其次，为了有效地学习此类决策边界，我们建议利用所见类的背景功能。由于这些背景区域没有显着促进近距离分类的决定，因此自然地将它们用作分类器学习的伪阶层。我们的广泛实验表明，我们提出的方法不仅要优于多个基线，而且还为三个流行的基准测试（即Tieredimagenet，Miniimagenet和Caltech-uscd Birds-birds-2011-2011（Cub））设定了新的最先进结果。

Few-shot object detection (FSOD), which aims at learning a generic detector that can adapt to unseen tasks with scarce training samples, has witnessed consistent improvement recently. However, most existing methods ignore the efficiency issues, e.g., high computational complexity and slow adaptation speed. Notably, efficiency has become an increasingly important evaluation metric for few-shot techniques due to an emerging trend toward embedded AI. To this end, we present an efficient pretrain-transfer framework (PTF) baseline with no computational increment, which achieves comparable results with previous state-of-the-art (SOTA) methods. Upon this baseline, we devise an initializer named knowledge inheritance (KI) to reliably initialize the novel weights for the box classifier, which effectively facilitates the knowledge transfer process and boosts the adaptation speed. Within the KI initializer, we propose an adaptive length re-scaling (ALR) strategy to alleviate the vector length inconsistency between the predicted novel weights and the pretrained base weights. Finally, our approach not only achieves the SOTA results across three public benchmarks, i.e., PASCAL VOC, COCO and LVIS, but also exhibits high efficiency with 1.8-100x faster adaptation speed against the other methods on COCO/LVIS benchmark during few-shot transfer. To our best knowledge, this is the first work to consider the efficiency problem in FSOD. We hope to motivate a trend toward powerful yet efficient few-shot technique development. The codes are publicly available at https://github.com/Ze-Yang/Efficient-FSOD.

光保护综合技术的快速进展达到了真实和操纵图像之间的边界开始模糊的临界点。最近，一个由Mega-Scale Deep Face Forgery DataSet，由290万个图像组成和221,247个视频的伪造网络已被释放。它是迄今为止的数据规模，操纵（7个图像级别方法，8个视频级别方法），扰动（36个独立和更混合的扰动）和注释（630万个分类标签，290万操纵区域注释和221,247个时间伪造段标签）。本文报告了Forgerynet-Face Forgery Analysis挑战2021的方法和结果，它采用了伪造的基准。模型评估在私人测试集上执行离线。共有186名参加比赛的参与者，11名队伍提交了有效的提交。我们将分析排名排名的解决方案，并展示一些关于未来工作方向的讨论。

在这项工作中，我们解决了艰巨的任务。几次射击语义细分至关重要，以充分利用支持信息。以前的方法通常采用蒙版的平均池在支持功能上，以将支持线索作为全球向量提取，通常由显着部分主导并失去了某些基本线索。在这项工作中，我们认为希望每个支持像素的信息都需要传输到所有查询像素，并提出一个具有最佳传输匹配模块的对应匹配网络（CMNET），以挖掘查询和支持图像之间的对应关系。此外，从注释的支持图像中充分利用本地和全局信息至关重要。为此，我们提出了一个消息流模块，以沿着同一图像内的内流传播消息，并在支持图像和查询图像之间进行交叉流，这极大地有助于增强本地特征表示。 Pascal VOC 2012，Coco女士和FSS-1000数据集的实验表明，我们的网络实现了新的最先进的少细分性能。

The goal of 3D pose transfer is to transfer the pose from the source mesh to the target mesh while preserving the identity information (e.g., face, body shape) of the target mesh. Deep learning-based methods improved the efficiency and performance of 3D pose transfer. However, most of them are trained under the supervision of the ground truth, whose availability is limited in real-world scenarios. In this work, we present X-DualNet, a simple yet effective approach that enables unsupervised 3D pose transfer. In X-DualNet, we introduce a generator $G$ which contains correspondence learning and pose transfer modules to achieve 3D pose transfer. We learn the shape correspondence by solving an optimal transport problem without any key point annotations and generate high-quality meshes with our elastic instance normalization (ElaIN) in the pose transfer module. With $G$ as the basic component, we propose a cross consistency learning scheme and a dual reconstruction objective to learn the pose transfer without supervision. Besides that, we also adopt an as-rigid-as-possible deformer in the training process to fine-tune the body shape of the generated results. Extensive experiments on human and animal data demonstrate that our framework can successfully achieve comparable performance as the state-of-the-art supervised approaches.

In this paper we present a novel multi-attribute face manipulation method based on textual descriptions. Previous text-based image editing methods either require test-time optimization for each individual image or are restricted to single attribute editing. Extending these methods to multi-attribute face image editing scenarios will introduce undesired excessive attribute change, e.g., text-relevant attributes are overly manipulated and text-irrelevant attributes are also changed. In order to address these challenges and achieve natural editing over multiple face attributes, we propose a new decoupling training scheme where we use group sampling to get text segments from same attribute categories, instead of whole complex sentences. Further, to preserve other existing face attributes, we encourage the model to edit the latent code of each attribute separately via an entropy constraint. During the inference phase, our model is able to edit new face images without any test-time optimization, even from complex textual prompts. We show extensive experiments and analysis to demonstrate the efficacy of our method, which generates natural manipulated faces with minimal text-irrelevant attribute editing. Code and pre-trained model will be released.